path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74061207/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum()
plot_1 = sns.histplot(data=df, x='Ship_Mode')
plt.show()
plot_2 = sns.histplot(data=df, x='Order_Priority')
plt.show()
plot_3 = sns.histplot(data=df... | code |
74061207/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum()
plot_1=sns.histplot(data=df, x='Ship_Mode')
plt.show()
plot_2=sns.histplot(data=df, x='Order_Priority')
plt.show()
plot_3=sns.histplot(data=df, x='C... | code |
74061207/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum()
plot_1=sns.histplot(data=df, x='Ship_Mode')
plt.show()
plot_2=sns.histplot(data=df, x='Order_Priority')
plt.show()
plot_3=sns.histplot(data=df, x='C... | code |
74061207/cell_3 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
print(df.shape)
df.head() | code |
74061207/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum()
print(df.Order_Priority.unique(), df.Ship_Mode.unique(), df.Region.unique(), df.Customer_Segment.unique(), df.Product_Category.unique(), df.Product_Container.unique()) | code |
74061207/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum()
plot_1=sns.histplot(data=df, x='Ship_Mode')
plt.show()
plot_2=sns.histplot(data=df, x='Order_Priority')
plt.show()
plot_3=sns.hi... | code |
50224445/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
fraud_data = data[data['isFraud'] == 1]
fraud_data.head() | code |
50224445/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
data[data['isFraud'] == 1]['type'].unique() | code |
50224445/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
100 * data['isFraud'].value_counts() / len(data) | code |
50224445/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
data.info() | code |
50224445/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
fraud_data = data[data['isFraud'] == 1]
safe_data = data[data['isFraud'] == 0]
sampled_data = safe_data.sample(n=len(fraud_data))
df = pd.concat([fraud_data, sampled_dat... | code |
50224445/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
fraud_data = data[data['isFraud'] == 1]
safe_data = data[data['isFraud'] == 0]
sampled_data = safe_data.sample(n=len(fraud_data))
df = pd.concat([fraud_data, sampled_dat... | code |
50224445/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
data['type'].unique() | code |
50224445/cell_50 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train) | code |
50224445/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
from sklearn.metrics import accuracy_score, f1_score... | code |
50224445/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50224445/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
fraud_data = data[data['isFraud'] == 1]
safe_data = data[data['isFraud'] == 0]
sampled_data = safe_data.sample(n=len(fraud_data))
df = pd.concat([fraud_data, sampled_dat... | code |
50224445/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
from sklearn.metrics import accuracy_score, f1_score... | code |
50224445/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
data['isFraud'].value_counts() | code |
50224445/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
data['isFlaggedFraud'].value_counts() | code |
50224445/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
fraud_data = data[data['isFraud'] == 1]
safe_data = data[data['isFraud'] == 0]
sampled_data = safe_data.sample(n=len(fraud_data))
df = pd.concat([fraud_data, sampled_dat... | code |
50224445/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/paysim1/PS_20174392719_1491204439457_log.csv')
fraud_data = data[data['isFraud'] == 1]
len(fraud_data) | code |
50224445/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
from sklearn.metrics import accuracy_score, f1_score... | code |
2042925/cell_9 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode... | code |
2042925/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode... | code |
2042925/cell_20 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode... | code |
2042925/cell_6 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode... | code |
2042925/cell_16 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode... | code |
2042925/cell_14 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode... | code |
74056226/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74056226/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
74056226/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def percent_missing(df: pd.DataFrame):
totalCells = np.product(df.shape)
missingCount = df.isnull().sum()
... | code |
130005876/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from usgs_scraper import extra_data_scraper
import pandas as pd
import re
import requests
import sys
"""
Get all the monitoring locations for a state from the USGS Water Services API.
Input:
The state we want data from (Arizona, New York, etc.)
Output:
A CSV of all monitoring... | code |
104120001/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nyra_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
nyra_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
nyra_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.c... | code |
104120001/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
!pip install pymap3d
import pymap3d as pm
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
# Input data files are available in t... | code |
104120001/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pymap3d as pm
nyra_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
nyra_start = pd.re... | code |
104120001/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pymap3d as pm
nyra_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
nyra_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
nyra_race = pd.re... | code |
1009451/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import model_from_json
from keras.optimizers import SGD
import cv2
import cv2
import glob
import glob
import matplotlib.pypl... | code |
1009451/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import os
import random
import random
labels = [1, 2, 3]
count = 0
for l in labels:
train_files = ['../input/train/Type_' + str(l) + '/' + f for f in os.listdir('../input/train/Type_' + str(l) + '/')]
random_f... | code |
1009451/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(2016)
import os
import glob
import cv2
import math
import pickle
import datetime
import pandas as pd
import statistics
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import KFold
from keras.models ... | code |
1009451/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import cv2
import glob
import glob
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import os
import random
import random
labels = [1, 2, 3]
count = 0
for l in labels:
train_files = ['../input/train/Type_' + str(l) + '/' + f for f in os.listdir('../input/train/Type_' + s... | code |
34128064/cell_13 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_9 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/youtube-new/USvideos.csv')
print(df.columns) | code |
34128064/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(['category_id']).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_7 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128064/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/youtube-new/USvideos.csv')
df.head() | code |
105207443/cell_42 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
def pie_plot(df, cols_list, rows, cols):
fig, axes = plt.subplo... | code |
105207443/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.head() | code |
105207443/cell_29 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
df_c = df.copy()
df_c = df_c.drop_duplicates()
df_c = pd.get_dummies(df_c, columns=['... | code |
105207443/cell_39 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
def pie_plot(df, cols_list, rows, cols):
fig, axes = plt.subplo... | code |
105207443/cell_41 | [
"image_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
df_c = df.c... | code |
105207443/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
df_c = df.copy()
df_c = df_c.drop_duplicates()
print('Before dropping duplicates {} a... | code |
105207443/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.info() | code |
105207443/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
def pie_plot(df, cols_list, rows, cols):
fig, axes = plt.subplots(rows, cols)
f... | code |
105207443/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'}) | code |
105207443/cell_38 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
def pie_plot(df, cols_list, rows, cols):
fig, axes = plt.subplo... | code |
105207443/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
df_c = df.copy()
df_c = df_c.drop_duplicates()
df_c = pd.get_dummies(df_c, columns=['... | code |
105207443/cell_46 | [
"image_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
df_c = df.c... | code |
105207443/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
sns.heatmap(df.isnull()) | code |
105207443/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
df.hist(bins=200, figsize=[20, 10]) | code |
105207443/cell_37 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
def pie_plot(df, cols_list, rows, cols):
fig, axes = plt.subplo... | code |
105207443/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.describe(include='all').style.background_gradient(cmap='Blues').set_properties(**{'font-family': 'Segoe UI'})
def pie_plot(df, cols_list, rows, cols):
fig, axes = plt.subplots(rows, cols)
for ax, col in zip(axes.... | code |
72065687/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
test.shape
train.isnull().sum()
test.isnull().sum()
train.dropna(inplace=True... | code |
72065687/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape | code |
72065687/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
test.shape
train.isnull().sum()
test.isnull().sum()
train.dropna(inplace=True... | code |
72065687/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape | code |
72065687/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.s... | code |
72065687/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(figsize=(20, 20)) | code |
72065687/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.s... | code |
72065687/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.info() | code |
72065687/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape
travel_dum = pd.g... | code |
72065687/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72065687/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum() | code |
72065687/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.s... | code |
72065687/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape
travel_dum = pd.g... | code |
72065687/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
test.shape
test.isnull().sum() | code |
72065687/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape
train.head() | code |
72065687/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape
train.info() | code |
72065687/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.head() | code |
72065687/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
test.shape
test.isnull().sum()
test.dropna(inplace=True)
test.shape
test.info() | code |
72065687/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.s... | code |
72065687/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape
travel_dum = pd.g... | code |
72065687/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
test.shape
test.isnull().sum()
test.dropna(inplace=True)
test.shape | code |
72065687/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
train.shape
train.isnull().sum()
train.dropna(inplace=True)
train.shape
travel_dum = pd.g... | code |
72065687/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv')
test.shape | code |
128010675/cell_30 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from torch.utils.data import DataLoader
from transformers import TrainingArguments, Trainer
from transformers import ViTForImageClassification
from transformers import ... | code |
128010675/cell_6 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from datasets import load_dataset
train_ds = load_dataset('miladfa7/5-Flower-Types-Classification-Dataset')
train_ds = train_ds['train'].train_test_split(test_size=0.15)
train_data = train_ds['train']
test_data = train_ds['test']
train_data[52]['image'] | code |
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